Looking inside the Ocean: Toward an Autonomous Imaging System for Monitoring Gelatinous Zooplankton

Abstract

Marine plankton abundance and dynamics in the open and interior ocean is still an unknown field. The knowledge of gelatinous zooplankton distribution is especially challenging, because this type of plankton has a very fragile structure and cannot be directly sampled using traditional net based techniques. To overcome this shortcoming, Computer Vision techniques can be successfully used for the automatic monitoring of this group.This paper presents the GUARD1 imaging system, a low-cost stand-alone instrument for underwater image acquisition and recognition of gelatinous zooplankton, and discusses the performance of three different methodologies, Tikhonov Regularization, Support Vector Machines and Genetic Programming, that have been compared in order to select the one to be run onboard the system for the automatic recognition of gelatinous zooplankton. The performance comparison results highlight the high accuracy of the three methods in gelatinous zooplankton identification, showing their good capability in robustly selecting relevant features. In particular, Genetic Programming technique achieves the same performances of the other two methods by using a smaller set of features, thus being the most efficient in avoiding computationally consuming preprocessing stages, that is a crucial requirement for running on an autonomous imaging system designed for long lasting deployments, like the GUARD1. The Genetic Programming algorithm has been installed onboard the system, that has been operationally tested in a two-months survey in the Ligurian Sea, providing satisfactory results in terms of monitoring and recognition performances.

Exo-Ocean Exploration with Deep-Sea Sensor and Platform Technologies

“…High-definition (HD) imaging is widely used in ecological exploration of Earth's deep-sea, and current tools may be used to identify the presence of fauna with sessile or motile morphological characteristics on icy moons, although that possibility is to date still highly uncertain (Newman, 2018). Within this context, fast-developing deep-sea imaging technologies centered on HD photogrammetry, stereo, hyperspectral, miniaturized cameras and low-light vision are established tools that permit assessment of the presence and activity of organisms (e.g., Kokubun et al, 2013;Bicknell et al, 2016;Corgnati et al, 2016, Marini et al, 2018a. These imaging assets could be adapted for the identification of exo-oceanic fauna in a broad range of sizes (i.e., equivalent to our prokaryotes, including bacterial mat formations, as well as micro-eukaryotes, micro-and meso-zooplankton, up to larger multicellular organisms).…”

“…This problem happens also in deep-sea research, where solutions are provided by data science, pattern analysis, and artificial intelligence methodologies (Skiena, 2017;Aguzzi et al, 2019). Simple computer vision algorithms can be executed on board platforms' imaging asset, to identify any subject different from the water or seabed itself (Corgnati et al, 2016;Marini et al, 2018a). General approaches based on image enhancement, differencing, and background subtraction methods can be used to discard irrelevant information (Moeslund, 2012;Peters, 2017); for example, in the case of water column, ice shell, or seabed surfaces, changes in patterns would be slower with respect to traveling objects.…”

Towards Naples Ecological REsearch for Augmented Observatories (NEREA): The NEREA-Fix Module, a Stand-Alone Platform for Long-Term Deep-Sea Ecosystem Monitoring

“…These algorithms will be used for recognizing and classifying the macrofauna-and megafauna-relevant subjects contained in the acquired optical and acoustic images. Images without any relevant content will be discarded [61,62] without being communicated at the land station in order to reduce data transfer loads. Content-based image analysis algorithms will be learned at the land laboratory based on images acquired by the SP.…”

No more reason for ignoring gelatinous zooplankton in ecosystem assessment and marine management: Concrete cost-effective methodology during routine fishery trawl surveys

“…Today the opportunistic use of fishery surveys provides the only large spatio-temporal sampling tool of GZ and so at low cost. New video systems have been developed and seem promising for the monitoring of jellyfish [46], but their cost-effectiveness need to be addressed.…”